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相关概念视频

Reversible and Irreversible Processes01:14

Reversible and Irreversible Processes

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The thermodynamic processes can be classified into reversible and irreversible processes. The processes that can be restored to their initial state are called reversible processes. It is only possible if the process is in quasi-static equilibrium, i.e., it takes place in infinitesimally small steps, and the system remains at equilibrium However, these are ideal processes and do not occur naturally. An ideal system undergoing a reversible process is always in thermodynamic equilibrium within...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Cyclic Processes And Isolated Systems01:19

Cyclic Processes And Isolated Systems

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A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state. 
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each...
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Fermi Level Dynamics01:12

Fermi Level Dynamics

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The vacuum level denotes the energy threshold required for an electron to escape from a material surface. It is usually positioned above the conduction band of a semiconductor and acts as a benchmark for comparing electron energies within various materials.
Electron affinity in semiconductors refers to the energy gap between the minimum of its conduction band and the vacuum level and it is a critical parameter in determining how easily a semiconductor can accept additional electrons.
The work...
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Precipitate Formation and Particle Size Control01:16

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In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
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相关实验视频

Updated: Jan 17, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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使用灵活触发内核的霍克斯过程.

Yamac Isik1, Paidamoyo Chapfuwa2, Connor Davis3

  • 1Department of Biostatistcs and Bioinformatics, Duke University, Durham, USA.

Proceedings of machine learning research
|September 19, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种高效的霍克斯过程模型,使用触发内核而不是复杂的注意力机制. 新模型提高了事件序列建模的预测准确性和计算效率.

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科学领域:

  • 计算统计学 计算统计学
  • 机器学习 机器学习
  • 点处理过程 点处理过程

背景情况:

  • 对于霍克斯过程,以变压器为灵感的模型提供了改进的预测,但却受到高计算复杂性的困扰.
  • 现有的模型很难充分捕捉点过程中固有的触发函数.

研究的目的:

  • 在霍克斯过程建模中开发一种高效和通用的方法来编码历史事件序列.
  • 为了解决高计算成本和当前基于变压器的模型中不充分捕获触发函数的局限性.

主要方法:

  • 复杂的注意力结构被从观察到的数据中获得的触发核心所取代.
  • 在触发函数估计器中纳入了sigmoid门机制,用于局部在时间效应.
  • 使用事件类型表示和时间嵌入学习了类型时间内核参数.

主要成果:

  • 拟议的模型在合成和现实世界数据集的现有方法相比显示出更高的性能.
  • 在计算效率方面取得了显著的改进,并减少了内存的复杂性.
  • 通过直接应用新的触发内核,提供了可解释的结果.

结论:

  • 新的霍克斯过程模型为基于变压器的架构提供了高效和有效的替代方案.
  • 使用带有 sigmoid gating 的触发内核可以增强时间依赖和事件触发的建模.
  • 该模型的可解释性和性能使其适用于各种应用,包括流行病学数据分析.